
We enable the use of calibrated pixel values read from image files in GeoTIFF format. This is necessary since we work with images with pixel values that carry information about physical entities and represent either calibrated \(\sigma _0\)  values (backscatter coefficients) or AGB predictions measured in \(\mathrm {Mg\,ha}^{-1}\) .

We change the activation function in the output layer from a hyperbolic tangent (tanh) function used in [1]} to a rectified linear unit (ReLU) activation function. In an earlier phase of this work [2]}, we noticed that the tanh activation function we used in the output layer generated AGB values that were clearly overestimating the ALS-based AGB predictions from [3]}, and particularly failed to predict AGB values close to zero. An important criterion for our cGAN regression model is that it should be able to predict zero biomass to correlate well with ground reference AGB in non-vegetated areas. The overprediction observed in [2]} can be explained by the nature of the tanh activation function. As the range of the tanh function is [0.0, 1.0], it implies that all data introduced to the cGAN need to be normalised to the same range. The tanh function must output exactly zero to predict zero biomass, which only happens when the action potential goes to \(-\infty \) . This explains why prediction with the tanh function seems to clip the AGB values at a level higher than zero.

